CAREER: Computational Design and Optimization of Operationally Robust Crystal Nucleating Materials via Surface Nano-Patterning

Yale University, New Haven CT

Investigators

Abstract

Single crystalline materials are in high demand for a variety of applications in the electronics, solar energy conversion and aerospace industries. Large-scale growth of single crystals typically requires conducting crystallization under conditions at which the nucleation rate is slow enough to prevent the formation of polycrystalline particles. This process is time consuming and expensive. The proposed research is aimed at using advanced computational techniques to study the use of engineered surfaces, called crystal nucleating agents (CNAs), for accelerating the nucleation and growth of single crystals. The proposed studies aim to develop fundamental theories that can be used for optimizing crystal growth of a variety of materials. The main objective of this project is to develop a fundamental understanding of the effects of nanoscale patterning of a CNA surface on crystal nucleation and to identify operating conditions leading to reduced sensitivity of the nucleation rate to changes in process variables over a wide range of operating conditions within the nucleation-limited regime. This previously unknown deviation from the classical picture of heterogeneous nucleation is interesting to explore and understand from a theoretical perspective. The proposed research will computationally explore how different features of a nano-patterned CNA surface affect the sensitivity of nucleation rate to temperature and concentration in the nucleation-limited regime, and use that knowledge to design optimal CNA surfaces that offer the smallest sensitivity of rate to operating conditions at specific nucleation rates. The insights obtained from such theoretical and computational investigations can result in design rules that can guide experimental efforts to design operationally robust CNA materials. Integration of research and education will be focused towards developing a new course on Computational Materials Science for graduate and undergraduate students. Outreach efforts will involve public presentations and programming workshops targeting underrepresented minority students to motivate them to pursue career paths in STEM fields. The proposed project also involves the development of an advanced sampling software package that will be publicly shared with researchers that study rare events in a wide variety of physical, chemical and biological systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.